From enrollment to a Springer paper and an international conference talk in 2 months
Ashwajit developed the Replay-Gated Cascade Consolidation model, a 1,000-neuron Izhikevich spiking network with spike-timing-dependent plasticity and Fusi-type slow cascade weights. He stated three predictions in advance and confirmed all three across more than 700 simulation runs, scaling up to 5,000 neurons. Removing replay dropped retention from 0.286 to 0.037, and the harmonic-series prediction fit with an R squared of 0.828. He presented the work himself at BICA 2026 in Merida, Mexico, a 15-minute talk followed by 5 minutes of questions from the room.
FIELDComputational Neuroscience
RESULTSpringer published, presented at BICA 2026 in Mexico
VENUESpringer Lecture Notes in Electrical Engineering, BICA 2026, 2026
Ashwajit Warwatkar, YRI FellowSPRINGER
BEFORE THE FELLOWSHIP
Early-stage research experience with independent projects in biomedical AI and wetware computing, but no peer-reviewed publications.
AFTER
First-author Springer publication on computational neuroscience, and a 15-minute talk delivered at an international conference in Mexico, two months from enrollment.
THE LEDGER
01Presented at BICA 2026 in Merida, Mexico, September 2026: a 15-minute talk plus 5 minutes of questions
02Selected for oral presentation, the highest presentation tier at the conference
03Published in Springer Lecture Notes in Electrical Engineering, Scopus and EI Compendex indexed
04Confirmed 3 of 3 pre-stated predictions across more than 700 simulation runs
05Reviewers scored the work 5 out of 5 for novelty and significance
06Two months from YRI enrollment to Springer acceptance, at 17 years old
